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feat: Support NVIDIA PixelDiT and PiD (CORE-201) (#14103)
This commit is contained in:
Jukka Seppänen
GitHub
parent
921775704c
commit
28f4ef277c
@ -799,13 +799,15 @@ class ZImagePixelSpace(ChromaRadiance):
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"""
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pass
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class HiDreamO1Pixel(ChromaRadiance):
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"""Pixel-space latent format for HiDream-O1.
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No VAE — model patches/unpatches raw RGB internally with patch_size=32.
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"""
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pass
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class PixelDiTPixel(ChromaRadiance):
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pass
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class CogVideoX(LatentFormat):
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"""Latent format for CogVideoX-2b (THUDM/CogVideoX-2b).
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@ -211,7 +211,7 @@ class TimestepEmbedder(nn.Module):
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Embeds scalar timesteps into vector representations.
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"""
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def __init__(self, hidden_size, frequency_embedding_size=256, output_size=None, dtype=None, device=None, operations=None):
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def __init__(self, hidden_size, frequency_embedding_size=256, output_size=None, dtype=None, device=None, operations=None, max_period=10000):
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super().__init__()
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if output_size is None:
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output_size = hidden_size
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@ -221,9 +221,10 @@ class TimestepEmbedder(nn.Module):
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operations.Linear(hidden_size, output_size, bias=True, dtype=dtype, device=device),
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)
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self.frequency_embedding_size = frequency_embedding_size
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self.max_period = max_period
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def forward(self, t, dtype, **kwargs):
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t_freq = timestep_embedding(t, self.frequency_embedding_size).to(dtype)
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t_freq = timestep_embedding(t, self.frequency_embedding_size, max_period=self.max_period).to(dtype)
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t_emb = self.mlp(t_freq)
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return t_emb
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239
comfy/ldm/pixeldit/model.py
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239
comfy/ldm/pixeldit/model.py
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@ -0,0 +1,239 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import comfy.ldm.common_dit
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import comfy.patcher_extension
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from comfy.ldm.flux.math import apply_rope, rope
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from comfy.ldm.hidream.model import FeedForwardSwiGLU
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from comfy.ldm.modules.attention import optimized_attention
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from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder
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from .modules import (
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FinalLayer,
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PatchTokenEmbedder,
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PiTBlock,
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PixelTokenEmbedder,
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apply_adaln_,
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precompute_freqs_cis_2d,
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)
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class MMDiTJointAttention(nn.Module):
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"""Joint MMDiT attention with separate Q/K/V/proj for image and text streams.
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RoPE is applied to each stream before concatenation so each stream uses its own
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2D/1D positional encoding. Concat order is [text, image] (text first).
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"""
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def __init__(self, dim, num_heads=8, qkv_bias=False, dtype=None, device=None, operations=None):
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super().__init__()
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assert dim % num_heads == 0
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self.num_heads = num_heads
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self.head_dim = dim // num_heads
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self.qkv_x = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
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self.qkv_y = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
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self.q_norm_x = operations.RMSNorm(self.head_dim, eps=1e-6, dtype=dtype, device=device)
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self.k_norm_x = operations.RMSNorm(self.head_dim, eps=1e-6, dtype=dtype, device=device)
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self.q_norm_y = operations.RMSNorm(self.head_dim, eps=1e-6, dtype=dtype, device=device)
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self.k_norm_y = operations.RMSNorm(self.head_dim, eps=1e-6, dtype=dtype, device=device)
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self.proj_x = operations.Linear(dim, dim, dtype=dtype, device=device)
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self.proj_y = operations.Linear(dim, dim, dtype=dtype, device=device)
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def forward(self, x, y, pos_img, pos_txt=None, attn_mask=None, transformer_options={}):
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B, Nx, _ = x.shape
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_, Ny, _ = y.shape
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H = self.num_heads
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D = self.head_dim
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qkv_x = self.qkv_x(x).reshape(B, Nx, 3, H, D).permute(2, 0, 3, 1, 4)
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qx, kx, vx = qkv_x.unbind(0)
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qx = self.q_norm_x(qx)
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kx = self.k_norm_x(kx)
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qkv_y = self.qkv_y(y).reshape(B, Ny, 3, H, D).permute(2, 0, 3, 1, 4)
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qy, ky, vy = qkv_y.unbind(0)
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qy = self.q_norm_y(qy)
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ky = self.k_norm_y(ky)
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qx, kx = apply_rope(qx, kx, pos_img[None, None])
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if pos_txt is not None:
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qy, ky = apply_rope(qy, ky, pos_txt[None, None])
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q_joint = torch.cat([qy, qx], dim=2)
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k_joint = torch.cat([ky, kx], dim=2)
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v_joint = torch.cat([vy, vx], dim=2)
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out_joint = optimized_attention(
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q_joint, k_joint, v_joint, H,
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mask=attn_mask, skip_reshape=True, skip_output_reshape=True,
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transformer_options=transformer_options,
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)
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out_y = out_joint[:, :, :Ny, :].transpose(1, 2).reshape(B, Ny, H * D)
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out_x = out_joint[:, :, Ny:, :].transpose(1, 2).reshape(B, Nx, H * D)
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return self.proj_x(out_x), self.proj_y(out_y)
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class MMDiTBlockT2I(nn.Module):
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def __init__(self, hidden_size, groups, mlp_ratio=4.0, dtype=None, device=None, operations=None):
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super().__init__()
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self.norm_x1 = operations.RMSNorm(hidden_size, eps=1e-6, dtype=dtype, device=device)
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self.norm_y1 = operations.RMSNorm(hidden_size, eps=1e-6, dtype=dtype, device=device)
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self.attn = MMDiTJointAttention(hidden_size, num_heads=groups, qkv_bias=False, dtype=dtype, device=device, operations=operations)
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self.norm_x2 = operations.RMSNorm(hidden_size, eps=1e-6, dtype=dtype, device=device)
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self.norm_y2 = operations.RMSNorm(hidden_size, eps=1e-6, dtype=dtype, device=device)
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mlp_hidden_dim = int(hidden_size * mlp_ratio)
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self.mlp_x = FeedForwardSwiGLU(hidden_size, mlp_hidden_dim, multiple_of=1, dtype=dtype, device=device, operations=operations)
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self.mlp_y = FeedForwardSwiGLU(hidden_size, mlp_hidden_dim, multiple_of=1, dtype=dtype, device=device, operations=operations)
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self.adaLN_modulation_img = nn.Sequential(operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device))
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self.adaLN_modulation_txt = nn.Sequential(operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device))
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def forward(self, x, y, c, pos_img, pos_txt=None, attn_mask=None, transformer_options={}):
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shift_msa_x, scale_msa_x, gate_msa_x, shift_mlp_x, scale_mlp_x, gate_mlp_x = self.adaLN_modulation_img(c).chunk(6, dim=-1)
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shift_msa_y, scale_msa_y, gate_msa_y, shift_mlp_y, scale_mlp_y, gate_mlp_y = self.adaLN_modulation_txt(c).chunk(6, dim=-1)
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x_norm = apply_adaln_(self.norm_x1(x), shift_msa_x, scale_msa_x)
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y_norm = apply_adaln_(self.norm_y1(y), shift_msa_y, scale_msa_y)
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attn_x, attn_y = self.attn(x_norm, y_norm, pos_img, pos_txt, attn_mask, transformer_options=transformer_options)
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x = torch.addcmul(x, gate_msa_x, attn_x)
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y = torch.addcmul(y, gate_msa_y, attn_y)
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x = torch.addcmul(x, gate_mlp_x, self.mlp_x(apply_adaln_(self.norm_x2(x), shift_mlp_x, scale_mlp_x)))
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y = torch.addcmul(y, gate_mlp_y, self.mlp_y(apply_adaln_(self.norm_y2(y), shift_mlp_y, scale_mlp_y)))
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return x, y
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class PixDiT_T2I(nn.Module):
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"""PixelDiT T2I model. Hardcoded for the released 1024px Stage-3 checkpoint
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(also runs at 512px when fed the appropriate latent size and flow_shift).
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Forward:
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x: [B, 3, H, W] pixel-space input (no VAE)
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timesteps:[B] in [0, 1000] (ComfyUI flow sampling convention)
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context: [B, Ltxt, 2304] Gemma-2-2b-it hidden states (chi_prompt prepended)
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Returns flow-matching velocity [B, 3, H, W].
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"""
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def __init__(
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self,
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in_channels=3,
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num_groups=24,
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hidden_size=1536,
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pixel_hidden_size=16,
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pixel_attn_hidden_size=1152,
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pixel_num_groups=16,
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patch_depth=14,
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pixel_depth=2,
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patch_size=16,
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txt_embed_dim=2304,
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txt_max_length=300,
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use_text_rope=True,
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text_rope_theta=10000.0,
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image_model=None,
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dtype=None,
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device=None,
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operations=None,
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pixel_mlp_chunks=2,
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):
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super().__init__()
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self.dtype = dtype
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self.in_channels = in_channels
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self.out_channels = in_channels
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self.hidden_size = hidden_size
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self.num_groups = num_groups
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self.patch_depth = patch_depth
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self.pixel_depth = pixel_depth
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self.patch_size = patch_size
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self.pixel_hidden_size = pixel_hidden_size
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self.pixel_attn_hidden_size = pixel_attn_hidden_size
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self.pixel_num_groups = pixel_num_groups
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self.txt_embed_dim = txt_embed_dim
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self.txt_max_length = txt_max_length
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self.use_text_rope = use_text_rope
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self.text_rope_theta = text_rope_theta
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self.pixel_embedder = PixelTokenEmbedder(self.in_channels, self.pixel_hidden_size, dtype=dtype, device=device, operations=operations)
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self.s_embedder = PatchTokenEmbedder(self.in_channels * self.patch_size ** 2, self.hidden_size, bias=True, dtype=dtype, device=device, operations=operations)
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self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations, max_period=10)
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self.y_embedder = PatchTokenEmbedder(self.txt_embed_dim, self.hidden_size, bias=True, use_norm=True, dtype=dtype, device=device, operations=operations)
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self.y_pos_embedding = nn.Parameter(torch.empty(1, self.txt_max_length, self.hidden_size, dtype=dtype, device=device))
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self.patch_blocks = nn.ModuleList([
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MMDiTBlockT2I(self.hidden_size, self.num_groups,
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dtype=dtype, device=device, operations=operations)
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for _ in range(self.patch_depth)
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])
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self.pixel_blocks = nn.ModuleList([
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PiTBlock(
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self.pixel_hidden_size,
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self.hidden_size,
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patch_size=self.patch_size,
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num_heads=self.num_groups,
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attn_hidden_size=self.pixel_attn_hidden_size,
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attn_num_heads=self.pixel_num_groups,
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dtype=dtype, device=device, operations=operations,
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mlp_chunks=pixel_mlp_chunks,
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)
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for _ in range(self.pixel_depth)
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])
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self.final_layer = FinalLayer(self.pixel_hidden_size, self.out_channels, dtype=dtype, device=device, operations=operations)
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def _fetch_patch_pos(self, height, width, device, dtype, **rope_opts):
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return precompute_freqs_cis_2d(self.hidden_size // self.num_groups, height, width, device=device, dtype=dtype, **rope_opts)
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def _fetch_text_pos(self, length, device, dtype):
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return rope(torch.arange(length, dtype=torch.float32, device=device).reshape(1, -1), self.hidden_size // self.num_groups, self.text_rope_theta).squeeze(0).to(dtype=dtype)
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def forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, **kwargs):
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return comfy.patcher_extension.WrapperExecutor.new_class_executor(
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self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options),
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).execute(x, timesteps, context, attention_mask, transformer_options, **kwargs)
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def _pre_patch_block(self, s, i, **kwargs):
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"""Hook for subclasses to inject per-block state into the patch stream (e.g. PiD's LQ gate)."""
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return s
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def _forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, **kwargs):
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H_orig, W_orig = x.shape[2], x.shape[3]
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x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
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B, _, H, W = x.shape
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Hs = H // self.patch_size
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Ws = W // self.patch_size
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L = Hs * Ws
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pos_img = self._fetch_patch_pos(Hs, Ws, x.device, x.dtype, **(transformer_options.get("rope_options") or {}))
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x_patches = F.unfold(x, kernel_size=self.patch_size, stride=self.patch_size).transpose(1, 2)
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t_emb = self.t_embedder(timesteps.view(-1), x.dtype).view(B, -1, self.hidden_size)
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if context is None or context.dim() != 3:
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raise ValueError("PixDiT_T2I requires context (text embeddings) of shape [B, L, D]")
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Ltxt = min(context.shape[1], self.txt_max_length)
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y = context[:, :Ltxt, :]
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y_emb = self.y_embedder(y).view(B, Ltxt, self.hidden_size)
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y_emb = y_emb + self.y_pos_embedding[:, :Ltxt, :].to(y_emb) # y_pos_embedding is a raw nn.Parameter
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condition = F.silu(t_emb)
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pos_txt = self._fetch_text_pos(Ltxt, x.device, x.dtype) if self.use_text_rope else None
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s = self.s_embedder(x_patches)
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for i, blk in enumerate(self.patch_blocks):
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s = self._pre_patch_block(s, i, **kwargs)
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s, y_emb = blk(s, y_emb, condition, pos_img, pos_txt, None, transformer_options=transformer_options)
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s = F.silu(t_emb + s)
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s_cond = s.view(B * L, self.hidden_size)
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x_pixels = self.pixel_embedder(x, patch_size=self.patch_size)
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for blk in self.pixel_blocks:
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x_pixels = blk(x_pixels, s_cond, H, W, self.patch_size, mask=None, transformer_options=transformer_options)
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x_pixels = self.final_layer(x_pixels)
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C_out = self.out_channels
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P2 = self.patch_size * self.patch_size
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x_pixels = x_pixels.view(B, L, P2, C_out).permute(0, 3, 2, 1).reshape(B, C_out * P2, L)
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out = F.fold(x_pixels, (H, W), kernel_size=self.patch_size, stride=self.patch_size)
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return out[:, :, :H_orig, :W_orig]
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187
comfy/ldm/pixeldit/modules.py
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187
comfy/ldm/pixeldit/modules.py
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import torch
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import torch.nn as nn
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from comfy.ldm.flux.math import apply_rope, rope
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from comfy.ldm.modules.attention import optimized_attention
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from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, get_1d_sincos_pos_embed_from_grid_torch
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def apply_adaln_(x, shift, scale):
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return x.addcmul_(x, scale).add_(shift)
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def precompute_freqs_cis_2d(dim, height, width, theta=10000.0, scale=16.0,
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ref_grid_h=None, ref_grid_w=None,
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scale_x=1.0, scale_y=1.0, shift_x=0.0, shift_y=0.0,
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device=None, dtype=torch.float32, **kwargs):
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"""2D RoPE with x/y axis frequencies interleaved at stride 2 across head dim.
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rope_options:
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scale_x / scale_y multiply the position range (RoPE extrapolation).
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shift_x / shift_y offset the position origin (tiled / regional inference).
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With ref_grid_h/w set, also applies NTK-aware per-axis theta scaling
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(rope_mode='ntk_aware'): theta_axis = theta * (current/ref)^(dim_axis/(dim_axis-2)).
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Returns Flux-format rotation matrices of shape [H*W, dim/2, 2, 2].
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Layout of head-dim pairs: [x_0, y_0, x_1, y_1, ..., x_{dim/4-1}, y_{dim/4-1}].
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"""
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dim_axis = dim // 2
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if ref_grid_h is not None and dim_axis > 2:
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h_ntk = (height / ref_grid_h) ** (dim_axis / (dim_axis - 2))
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w_ntk = (width / ref_grid_w) ** (dim_axis / (dim_axis - 2))
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else:
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h_ntk = w_ntk = 1.0
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x_lin = torch.linspace(shift_x, scale * scale_x + shift_x, width, device=device)
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y_lin = torch.linspace(shift_y, scale * scale_y + shift_y, height, device=device)
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y_grid, x_grid = torch.meshgrid(y_lin, x_lin, indexing="ij")
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x_rope = rope(x_grid.reshape(1, -1), dim_axis, theta * w_ntk).squeeze(0)
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y_rope = rope(y_grid.reshape(1, -1), dim_axis, theta * h_ntk).squeeze(0)
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out = torch.stack([x_rope, y_rope], dim=2).reshape(height * width, dim // 2, 2, 2)
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return out.to(dtype=dtype)
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def get_2d_sincos_pos_embed(embed_dim, height, width, device=None, dtype=torch.float32):
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"""Standard 2D sin/cos absolute positional embedding (ViT-style).
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first half encodes W-coordinates, second half H.
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"""
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assert embed_dim % 4 == 0
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grid_h = torch.arange(height, dtype=torch.float32, device=device)
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grid_w = torch.arange(width, dtype=torch.float32, device=device)
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grid_y, grid_x = torch.meshgrid(grid_h, grid_w, indexing="ij")
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emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_x.reshape(-1), device=device)
|
||||
emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_y.reshape(-1), device=device)
|
||||
return torch.cat([emb_w, emb_h], dim=1).to(dtype=dtype)
|
||||
|
||||
|
||||
class RotaryAttention(nn.Module):
|
||||
"""Single-stream self-attention with rotary positional encoding (used inside PiTBlock)."""
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
assert dim % num_heads == 0
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
|
||||
self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, dtype=dtype, device=device)
|
||||
self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, dtype=dtype, device=device)
|
||||
self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x, pos, mask=None, transformer_options={}):
|
||||
B, N, C = x.shape
|
||||
H = self.num_heads
|
||||
D = self.head_dim
|
||||
qkv = self.qkv(x).reshape(B, N, 3, H, D).permute(2, 0, 3, 1, 4)
|
||||
q, k, v = qkv.unbind(0)
|
||||
q, k = apply_rope(self.q_norm(q), self.k_norm(k), pos[None, None])
|
||||
x = optimized_attention(q, k, v, H, mask=mask, skip_reshape=True, transformer_options=transformer_options)
|
||||
return self.proj(x)
|
||||
|
||||
|
||||
class FinalLayer(nn.Module):
|
||||
def __init__(self, hidden_size, out_channels, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
self.norm = operations.RMSNorm(hidden_size, eps=1e-6, dtype=dtype, device=device)
|
||||
self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x):
|
||||
return self.linear(self.norm(x))
|
||||
|
||||
|
||||
class PatchTokenEmbedder(nn.Module):
|
||||
"""Linear projection used both for patchified-image tokens and text-feature tokens."""
|
||||
def __init__(self, in_chans, embed_dim, use_norm=False, bias=True, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
self.proj = operations.Linear(in_chans, embed_dim, bias=bias, dtype=dtype, device=device)
|
||||
self.norm = operations.RMSNorm(embed_dim, eps=1e-6, dtype=dtype, device=device) if use_norm else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
return self.norm(self.proj(x))
|
||||
|
||||
|
||||
class PixelTokenEmbedder(nn.Module):
|
||||
"""Pixel-level embedder: lifts each RGB pixel to hidden_size and packs into per-patch sequences."""
|
||||
def __init__(self, in_channels, hidden_size_output, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.hidden_size_output = hidden_size_output
|
||||
self.proj = operations.Linear(self.in_channels, self.hidden_size_output, bias=True, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, inputs, patch_size):
|
||||
B, _, H, W = inputs.shape
|
||||
Hs, Ws = H // patch_size, W // patch_size
|
||||
P2 = patch_size * patch_size
|
||||
x = inputs.permute(0, 2, 3, 1).contiguous()
|
||||
x = self.proj(x)
|
||||
pos_full = get_2d_sincos_pos_embed(self.hidden_size_output, H, W, device=x.device, dtype=x.dtype).view(H, W, self.hidden_size_output)
|
||||
x = x + pos_full.unsqueeze(0)
|
||||
x = x.view(B, Hs, patch_size, Ws, patch_size, self.hidden_size_output)
|
||||
return x.permute(0, 1, 3, 2, 4, 5).reshape(B * Hs * Ws, P2, self.hidden_size_output)
|
||||
|
||||
|
||||
class PiTBlock(nn.Module):
|
||||
"""Pixel-level transformer block.
|
||||
|
||||
Compresses each patch's P^2 pixel tokens → 1 attention token via a linear,
|
||||
runs global self-attention across patches with 2D RoPE, then expands back to P^2 tokens.
|
||||
Conditioning is per-pixel adaLN from the patch-level features.
|
||||
"""
|
||||
def __init__(self, pixel_hidden_size, patch_hidden_size, patch_size, num_heads, mlp_ratio=4.0,
|
||||
attn_hidden_size=None, attn_num_heads=None, dtype=None, device=None, operations=None, mlp_chunks=1):
|
||||
super().__init__()
|
||||
self.pixel_dim = pixel_hidden_size
|
||||
self.context_dim = patch_hidden_size
|
||||
self.attn_dim = attn_hidden_size if attn_hidden_size is not None else patch_hidden_size
|
||||
self.num_heads = attn_num_heads if attn_num_heads is not None else num_heads
|
||||
assert self.attn_dim % self.num_heads == 0
|
||||
|
||||
p2 = patch_size * patch_size
|
||||
self.compress_to_attn = operations.Linear(p2 * self.pixel_dim, self.attn_dim, bias=True, dtype=dtype, device=device)
|
||||
self.expand_from_attn = operations.Linear(self.attn_dim, p2 * self.pixel_dim, bias=True, dtype=dtype, device=device)
|
||||
|
||||
self.norm1 = operations.RMSNorm(self.pixel_dim, eps=1e-6, dtype=dtype, device=device)
|
||||
self.attn = RotaryAttention(self.attn_dim, num_heads=self.num_heads, qkv_bias=False, dtype=dtype, device=device, operations=operations)
|
||||
self.norm2 = operations.RMSNorm(self.pixel_dim, eps=1e-6, dtype=dtype, device=device)
|
||||
self.mlp = Mlp(self.pixel_dim, hidden_features=int(self.pixel_dim * mlp_ratio), dtype=dtype, device=device, operations=operations)
|
||||
|
||||
self.adaLN_modulation_msa = operations.Linear(self.context_dim, 3 * self.pixel_dim * p2, bias=True, dtype=dtype, device=device)
|
||||
self.adaLN_modulation_mlp = operations.Linear(self.context_dim, 3 * self.pixel_dim * p2, bias=True, dtype=dtype, device=device)
|
||||
|
||||
self._rope_fn = precompute_freqs_cis_2d
|
||||
self.mlp_chunks = max(1, int(mlp_chunks))
|
||||
|
||||
def _fetch_pos(self, height, width, device, dtype, **rope_opts):
|
||||
return self._rope_fn(self.attn_dim // self.num_heads, height, width, device=device, dtype=dtype, **rope_opts)
|
||||
|
||||
def forward(self, x, s_cond, image_height, image_width, patch_size, mask=None, transformer_options={}):
|
||||
BL, P2, _ = x.shape
|
||||
Hs, Ws = image_height // patch_size, image_width // patch_size
|
||||
L = Hs * Ws
|
||||
B = BL // L
|
||||
|
||||
# Attention path uses only msa params; compute, use, free before mlp params allocate.
|
||||
msa_params = self.adaLN_modulation_msa(s_cond).view(BL, P2, 3 * self.pixel_dim)
|
||||
shift_msa, scale_msa, gate_msa = msa_params.chunk(3, dim=-1)
|
||||
|
||||
x_norm = apply_adaln_(self.norm1(x), shift_msa, scale_msa)
|
||||
x_flat = x_norm.view(BL, P2 * self.pixel_dim)
|
||||
|
||||
x_comp = self.compress_to_attn(x_flat).view(B, L, self.attn_dim)
|
||||
pos_comp = self._fetch_pos(Hs, Ws, x.device, x.dtype, **(transformer_options.get("rope_options") or {}))
|
||||
attn_out = self.attn(x_comp, pos_comp, mask=mask, transformer_options=transformer_options)
|
||||
attn_flat = self.expand_from_attn(attn_out.view(B * L, self.attn_dim))
|
||||
attn_exp = attn_flat.view(BL, P2, self.pixel_dim)
|
||||
x = torch.addcmul(x, gate_msa, attn_exp)
|
||||
del msa_params, shift_msa, scale_msa, gate_msa
|
||||
|
||||
mlp_params = self.adaLN_modulation_mlp(s_cond).view(BL, P2, 3 * self.pixel_dim)
|
||||
shift_mlp, scale_mlp, gate_mlp = mlp_params.chunk(3, dim=-1)
|
||||
gate_mlp = gate_mlp.contiguous() # detach from mlp_params so the del below frees shift+scale storage before the MLP
|
||||
mlp_input = apply_adaln_(self.norm2(x), shift_mlp, scale_mlp)
|
||||
del mlp_params, shift_mlp, scale_mlp
|
||||
|
||||
# MLP in chunks since the peak memory usage is huge here
|
||||
chunk_size = (BL + self.mlp_chunks - 1) // self.mlp_chunks
|
||||
for s in range(0, BL, chunk_size):
|
||||
e = min(s + chunk_size, BL)
|
||||
x[s:e].addcmul_(gate_mlp[s:e], self.mlp(mlp_input[s:e]))
|
||||
return x
|
||||
226
comfy/ldm/pixeldit/pid.py
Normal file
226
comfy/ldm/pixeldit/pid.py
Normal file
@ -0,0 +1,226 @@
|
||||
"""PiD — Pixel Diffusion Decoder. Decodes a Flux/SD3/Flux2/Z-Image latent
|
||||
directly to a 4x-upscaled image in 4 distilled flow-matching steps. PixDiT_T2I
|
||||
body + LQ projection branch injected before each MMDiT patch block.
|
||||
"""
|
||||
|
||||
from typing import List
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .model import PixDiT_T2I
|
||||
from .modules import precompute_freqs_cis_2d
|
||||
|
||||
|
||||
class SigmaAwareGatePerTokenPerDim(nn.Module):
|
||||
"""gate = sigmoid(content_proj(cat[x, lq]) - exp(log_alpha) * sigma); out = x + gate * lq.
|
||||
|
||||
Trained init gives ~0.88 gate at sigma=0, ~0.05 at sigma=1.
|
||||
"""
|
||||
|
||||
def __init__(self, dim: int, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
self.content_proj = operations.Linear(dim * 2, dim, dtype=dtype, device=device)
|
||||
self.log_alpha = nn.Parameter(torch.empty((), dtype=dtype, device=device))
|
||||
|
||||
def forward(self, x: torch.Tensor, lq: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor:
|
||||
content_logit = self.content_proj(torch.cat([x, lq], dim=-1))
|
||||
# log_alpha is a raw nn.Parameter -> doesn't auto-cast under dynamic VRAM.
|
||||
log_alpha = self.log_alpha.to(device=x.device, dtype=torch.float32)
|
||||
sigma_offset = -log_alpha.exp() * sigma.float().view(-1, 1, 1)
|
||||
gate = torch.sigmoid(content_logit + sigma_offset)
|
||||
return x + (gate * lq).to(x.dtype)
|
||||
|
||||
|
||||
class ResBlock(nn.Module):
|
||||
"""Pre-activation ResNet block: GN -> SiLU -> Conv -> GN -> SiLU -> Conv + skip."""
|
||||
|
||||
def __init__(self, channels: int, num_groups: int = 4, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
self.block = nn.Sequential(
|
||||
operations.GroupNorm(num_groups, channels, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.Conv2d(channels, channels, kernel_size=3, padding=1, dtype=dtype, device=device),
|
||||
operations.GroupNorm(num_groups, channels, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.Conv2d(channels, channels, kernel_size=3, padding=1, dtype=dtype, device=device),
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return x + self.block(x)
|
||||
|
||||
|
||||
class LQProjection2D(nn.Module):
|
||||
"""LQ latent -> per-block patch-aligned features for controlnet-style injection."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
latent_channels: int,
|
||||
hidden_dim: int = 512,
|
||||
out_dim: int = 1536,
|
||||
patch_size: int = 16,
|
||||
sr_scale: int = 4,
|
||||
latent_spatial_down_factor: int = 8,
|
||||
num_res_blocks: int = 4,
|
||||
num_outputs: int = 7,
|
||||
interval: int = 2,
|
||||
dtype=None, device=None, operations=None,
|
||||
):
|
||||
super().__init__()
|
||||
self.latent_channels = latent_channels
|
||||
self.hidden_dim = hidden_dim
|
||||
self.out_dim = out_dim
|
||||
self.patch_size = patch_size
|
||||
self.sr_scale = sr_scale
|
||||
self.latent_spatial_down_factor = latent_spatial_down_factor
|
||||
self.num_outputs = num_outputs
|
||||
self.interval = interval
|
||||
|
||||
z_to_patch_ratio = (sr_scale * latent_spatial_down_factor) / patch_size
|
||||
self.z_to_patch_ratio = z_to_patch_ratio
|
||||
if z_to_patch_ratio >= 1:
|
||||
self.latent_fold_factor = 0
|
||||
latent_proj_in_ch = latent_channels
|
||||
else:
|
||||
fold_factor = int(1 / z_to_patch_ratio)
|
||||
assert fold_factor * z_to_patch_ratio == 1.0
|
||||
self.latent_fold_factor = fold_factor
|
||||
latent_proj_in_ch = latent_channels * fold_factor * fold_factor
|
||||
|
||||
layers = [
|
||||
operations.Conv2d(latent_proj_in_ch, hidden_dim, kernel_size=3, padding=1, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.Conv2d(hidden_dim, hidden_dim, kernel_size=3, padding=1, dtype=dtype, device=device),
|
||||
]
|
||||
for _ in range(num_res_blocks):
|
||||
layers.append(ResBlock(hidden_dim, dtype=dtype, device=device, operations=operations))
|
||||
self.latent_proj = nn.Sequential(*layers)
|
||||
|
||||
self.output_heads = nn.ModuleList(
|
||||
[operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) for _ in range(num_outputs)]
|
||||
)
|
||||
self.gate_modules = nn.ModuleList(
|
||||
[SigmaAwareGatePerTokenPerDim(out_dim, dtype=dtype, device=device, operations=operations)
|
||||
for _ in range(num_outputs)]
|
||||
)
|
||||
|
||||
def is_gate_active(self, block_idx: int) -> bool:
|
||||
return block_idx % self.interval == 0
|
||||
|
||||
def output_index(self, block_idx: int) -> int:
|
||||
return block_idx // self.interval
|
||||
|
||||
def gate(self, x: torch.Tensor, lq_feature: torch.Tensor, sigma: torch.Tensor, out_idx: int) -> torch.Tensor:
|
||||
return self.gate_modules[out_idx](x, lq_feature, sigma)
|
||||
|
||||
def _align_latent_to_patch_grid(self, lq_latent: torch.Tensor, pH: int, pW: int) -> torch.Tensor:
|
||||
B, z_dim = lq_latent.shape[:2]
|
||||
if self.z_to_patch_ratio >= 1:
|
||||
if lq_latent.shape[2] != pH or lq_latent.shape[3] != pW:
|
||||
z_aligned = F.interpolate(lq_latent, size=(pH, pW), mode="nearest")
|
||||
else:
|
||||
z_aligned = lq_latent
|
||||
else:
|
||||
f = self.latent_fold_factor
|
||||
zH_expected, zW_expected = pH * f, pW * f
|
||||
if lq_latent.shape[2] != zH_expected or lq_latent.shape[3] != zW_expected:
|
||||
lq_latent = F.interpolate(lq_latent, size=(zH_expected, zW_expected), mode="nearest")
|
||||
z_aligned = lq_latent.reshape(B, z_dim, pH, f, pW, f).permute(0, 1, 3, 5, 2, 4)
|
||||
z_aligned = z_aligned.reshape(B, z_dim * f * f, pH, pW)
|
||||
return self.latent_proj(z_aligned)
|
||||
|
||||
def forward(self, lq_latent: torch.Tensor, target_pH: int, target_pW: int) -> List[torch.Tensor]:
|
||||
feat = self._align_latent_to_patch_grid(lq_latent, target_pH, target_pW)
|
||||
B, C, H, W = feat.shape
|
||||
tokens = feat.permute(0, 2, 3, 1).contiguous().view(B, H * W, C)
|
||||
return [head(tokens) for head in self.output_heads]
|
||||
|
||||
|
||||
class PidNet(PixDiT_T2I):
|
||||
"""PixDiT_T2I + LQ injection (one sigma-gated feature inserted before each patch block)."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
lq_latent_channels: int = 16,
|
||||
lq_hidden_dim: int = 512,
|
||||
lq_num_res_blocks: int = 4,
|
||||
lq_interval: int = 2,
|
||||
sr_scale: int = 4,
|
||||
latent_spatial_down_factor: int = 8,
|
||||
rope_ref_h: int = 1024, # NTK ref resolution in PIXEL units: 1024px / patch=16 -> grid_ref=64.
|
||||
rope_ref_w: int = 1024,
|
||||
image_model=None,
|
||||
dtype=None, device=None, operations=None,
|
||||
**pixdit_kwargs,
|
||||
):
|
||||
super().__init__(dtype=dtype, device=device, operations=operations, **pixdit_kwargs)
|
||||
|
||||
self.rope_ref_grid_h = rope_ref_h // self.patch_size
|
||||
self.rope_ref_grid_w = rope_ref_w // self.patch_size
|
||||
|
||||
# Parent's PiTBlocks were built with plain RoPE — swap in NTK-aware.
|
||||
def _pit_rope_fn(head_dim, h, w, device=None, dtype=torch.float32, **rope_opts):
|
||||
return precompute_freqs_cis_2d(head_dim, h, w, ref_grid_h=self.rope_ref_grid_h, ref_grid_w=self.rope_ref_grid_w, device=device, dtype=dtype, **rope_opts)
|
||||
for blk in self.pixel_blocks:
|
||||
blk._rope_fn = _pit_rope_fn
|
||||
|
||||
num_lq_outputs = (self.patch_depth + lq_interval - 1) // lq_interval
|
||||
self.lq_proj = LQProjection2D(
|
||||
latent_channels=lq_latent_channels,
|
||||
hidden_dim=lq_hidden_dim,
|
||||
out_dim=self.hidden_size,
|
||||
patch_size=self.patch_size,
|
||||
sr_scale=sr_scale,
|
||||
latent_spatial_down_factor=latent_spatial_down_factor,
|
||||
num_res_blocks=lq_num_res_blocks,
|
||||
num_outputs=num_lq_outputs,
|
||||
interval=lq_interval,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
operations=operations,
|
||||
)
|
||||
|
||||
def _fetch_patch_pos(self, height, width, device, dtype, **rope_opts):
|
||||
return precompute_freqs_cis_2d(
|
||||
self.hidden_size // self.num_groups,
|
||||
height, width,
|
||||
ref_grid_h=self.rope_ref_grid_h, ref_grid_w=self.rope_ref_grid_w,
|
||||
device=device, dtype=dtype, **rope_opts,
|
||||
)
|
||||
|
||||
def _pre_patch_block(self, s, i, pid_lq_features, pid_degrade_sigma, **kwargs):
|
||||
if not self.lq_proj.is_gate_active(i):
|
||||
return s
|
||||
out_idx = self.lq_proj.output_index(i)
|
||||
if out_idx >= len(pid_lq_features):
|
||||
return s
|
||||
return self.lq_proj.gate(s, pid_lq_features[out_idx], pid_degrade_sigma, out_idx)
|
||||
|
||||
def _forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, lq_latent=None, degrade_sigma=None, **kwargs):
|
||||
if lq_latent is None:
|
||||
raise ValueError("PidNet requires lq_latent — attach via PiDConditioning")
|
||||
expected_c = self.lq_proj.latent_channels
|
||||
if lq_latent.shape[1] != expected_c:
|
||||
raise ValueError(
|
||||
f"Input latent has {lq_latent.shape[1]} channels, this model variant expects {expected_c}. "
|
||||
f"Flux1/SD3 = 16 channels, Flux2 = 128 channels."
|
||||
)
|
||||
B = x.shape[0]
|
||||
Hs = x.shape[2] // self.patch_size
|
||||
Ws = x.shape[3] // self.patch_size
|
||||
|
||||
degrade_sigma = degrade_sigma.to(device=x.device, dtype=torch.float32).reshape(-1)
|
||||
if degrade_sigma.numel() == 1 and B > 1:
|
||||
degrade_sigma = degrade_sigma.expand(B).contiguous()
|
||||
|
||||
lq_features = self.lq_proj(lq_latent=lq_latent.to(x), target_pH=Hs, target_pW=Ws)
|
||||
|
||||
return super()._forward(
|
||||
x, timesteps,
|
||||
context=context, attention_mask=attention_mask,
|
||||
transformer_options=transformer_options,
|
||||
pid_lq_features=lq_features,
|
||||
pid_degrade_sigma=degrade_sigma,
|
||||
**kwargs,
|
||||
)
|
||||
@ -49,6 +49,8 @@ import comfy.ldm.hunyuan3d.model
|
||||
import comfy.ldm.hidream.model
|
||||
import comfy.ldm.chroma.model
|
||||
import comfy.ldm.chroma_radiance.model
|
||||
import comfy.ldm.pixeldit.model
|
||||
import comfy.ldm.pixeldit.pid
|
||||
import comfy.ldm.ace.model
|
||||
import comfy.ldm.omnigen.omnigen2
|
||||
import comfy.ldm.qwen_image.model
|
||||
@ -1397,6 +1399,36 @@ class ZImagePixelSpace(Lumina2):
|
||||
BaseModel.__init__(self, model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiTPixelSpace)
|
||||
self.memory_usage_factor_conds = ("ref_latents",)
|
||||
|
||||
|
||||
class PixelDiTT2I(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device,
|
||||
unet_model=comfy.ldm.pixeldit.model.PixDiT_T2I)
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = super().extra_conds(**kwargs)
|
||||
attention_mask = kwargs.get("attention_mask", None)
|
||||
if attention_mask is not None:
|
||||
out["attention_mask"] = comfy.conds.CONDRegular(attention_mask)
|
||||
return out
|
||||
|
||||
|
||||
class PiD(PixelDiTT2I):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
BaseModel.__init__(self, model_config, model_type, device=device,
|
||||
unet_model=comfy.ldm.pixeldit.pid.PidNet)
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = super().extra_conds(**kwargs)
|
||||
lq_latent = kwargs.get("lq_latent", None)
|
||||
if lq_latent is not None:
|
||||
out["lq_latent"] = comfy.conds.CONDRegular(lq_latent)
|
||||
degrade_sigma = kwargs.get("degrade_sigma", None)
|
||||
if degrade_sigma is not None:
|
||||
out["degrade_sigma"] = comfy.conds.CONDRegular(degrade_sigma)
|
||||
return out
|
||||
|
||||
|
||||
class WAN21(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)
|
||||
|
||||
@ -463,6 +463,23 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
|
||||
return dit_config
|
||||
|
||||
# PiD (Pixel Diffusion Decoder). Must check BEFORE plain PixelDiT_T2I.
|
||||
_lq_w_key = '{}lq_proj.latent_proj.0.weight'.format(key_prefix)
|
||||
if _lq_w_key in state_dict_keys:
|
||||
in_ch = int(state_dict[_lq_w_key].shape[1])
|
||||
_gate_prefix = '{}lq_proj.gate_modules.'.format(key_prefix)
|
||||
num_gates = len({k[len(_gate_prefix):].split('.')[0]
|
||||
for k in state_dict_keys if k.startswith(_gate_prefix)})
|
||||
dit_config = {"image_model": "pid",
|
||||
"lq_latent_channels": in_ch,
|
||||
"latent_spatial_down_factor": 16 if in_ch >= 64 else 8}
|
||||
if num_gates > 0:
|
||||
dit_config["lq_interval"] = (14 + num_gates - 1) // num_gates
|
||||
return dit_config
|
||||
|
||||
if '{}core.pixel_embedder.proj.weight'.format(key_prefix) in state_dict_keys: # PixelDiT T2I
|
||||
return {"image_model": "pixeldit_t2i"}
|
||||
|
||||
if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys and '{}noise_refiner.0.attention.k_norm.weight'.format(key_prefix) in state_dict_keys: # Lumina 2
|
||||
dit_config = {}
|
||||
dit_config["image_model"] = "lumina2"
|
||||
|
||||
10
comfy/sd.py
10
comfy/sd.py
@ -49,6 +49,7 @@ import comfy.text_encoders.lt
|
||||
import comfy.text_encoders.hunyuan_video
|
||||
import comfy.text_encoders.cosmos
|
||||
import comfy.text_encoders.lumina2
|
||||
import comfy.text_encoders.pixeldit
|
||||
import comfy.text_encoders.wan
|
||||
import comfy.text_encoders.hidream
|
||||
import comfy.text_encoders.ace
|
||||
@ -1285,6 +1286,7 @@ class CLIPType(Enum):
|
||||
LONGCAT_IMAGE = 26
|
||||
COGVIDEOX = 27
|
||||
LENS = 28
|
||||
PIXELDIT = 29
|
||||
|
||||
|
||||
|
||||
@ -1528,8 +1530,12 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
|
||||
clip_target.tokenizer = variant.tokenizer
|
||||
tokenizer_data["tokenizer_json"] = clip_data[0].get("tokenizer_json", None)
|
||||
elif te_model == TEModel.GEMMA_2_2B:
|
||||
clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))
|
||||
clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
|
||||
if clip_type == CLIPType.PIXELDIT:
|
||||
clip_target.clip = comfy.text_encoders.pixeldit.pixeldit_te(**llama_detect(clip_data))
|
||||
clip_target.tokenizer = comfy.text_encoders.pixeldit.PixelDiTGemma2Tokenizer
|
||||
else:
|
||||
clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))
|
||||
clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
|
||||
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
|
||||
elif te_model == TEModel.GEMMA_3_4B:
|
||||
clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b")
|
||||
|
||||
@ -30,6 +30,7 @@ import comfy.text_encoders.longcat_image
|
||||
import comfy.text_encoders.ernie
|
||||
import comfy.text_encoders.cogvideo
|
||||
import comfy.text_encoders.hidream_o1
|
||||
import comfy.text_encoders.pixeldit
|
||||
|
||||
from . import supported_models_base
|
||||
from . import latent_formats
|
||||
@ -1201,6 +1202,72 @@ class ZImagePixelSpace(ZImage):
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
return model_base.ZImagePixelSpace(self, device=device)
|
||||
|
||||
class PixelDiTT2I(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"image_model": "pixeldit_t2i",
|
||||
}
|
||||
|
||||
unet_extra_config = {}
|
||||
|
||||
sampling_settings = {
|
||||
"shift": 4.0, # 1024px stage 3 default; 2.0 for 512px
|
||||
}
|
||||
|
||||
latent_format = latent_formats.PixelDiTPixel
|
||||
memory_usage_factor = 0.18
|
||||
supported_inference_dtypes = [torch.bfloat16, torch.float32]
|
||||
|
||||
vae_key_prefix = ["vae."]
|
||||
text_encoder_key_prefix = ["text_encoders."]
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
return model_base.PixelDiTT2I(self, device=device)
|
||||
|
||||
def process_unet_state_dict(self, state_dict):
|
||||
# pixel_dim from pixel_embedder.proj.weight = (pixel_dim, in_channels); p2 derived per-weight from total // (6 * pixel_dim).
|
||||
pixel_dim = next(v for k, v in state_dict.items() if k.endswith("pixel_embedder.proj.weight")).shape[0]
|
||||
|
||||
out = {}
|
||||
marker = ".adaLN_modulation.0."
|
||||
for k, v in state_dict.items():
|
||||
if k.startswith("_repa_projector") or k.startswith("net_ema."):
|
||||
continue
|
||||
if k.startswith("core."):
|
||||
k = k[len("core."):]
|
||||
elif k.startswith("net."):
|
||||
k = k[len("net."):]
|
||||
if "pixel_blocks." in k and marker in k:
|
||||
# Split into msa (chunks 0-2) and mlp (chunks 3-5) for the two-Linear PiTBlock to reduce peak VRAM
|
||||
p2 = v.shape[0] // (6 * pixel_dim)
|
||||
trail = v.shape[1:] # () for bias, (in_dim,) for weight
|
||||
vv = v.view(p2, 6, pixel_dim, *trail)
|
||||
base, suffix = k.split(marker)
|
||||
out[f"{base}.adaLN_modulation_msa.{suffix}"] = vv[:, 0:3].reshape(3 * p2 * pixel_dim, *trail).contiguous()
|
||||
out[f"{base}.adaLN_modulation_mlp.{suffix}"] = vv[:, 3:6].reshape(3 * p2 * pixel_dim, *trail).contiguous()
|
||||
else:
|
||||
out[k] = v
|
||||
return out
|
||||
|
||||
def clip_target(self, state_dict={}):
|
||||
return supported_models_base.ClipTarget(
|
||||
comfy.text_encoders.pixeldit.PixelDiTGemma2Tokenizer,
|
||||
comfy.text_encoders.pixeldit.PixelDiTGemma2TE,
|
||||
)
|
||||
|
||||
class PiD(PixelDiTT2I):
|
||||
unet_config = {
|
||||
"image_model": "pid",
|
||||
}
|
||||
|
||||
sampling_settings = {
|
||||
"shift": 1.5, # close approximation of the original distill 4 steps [0.999, 0.866, 0.634, 0.342, 0]
|
||||
}
|
||||
|
||||
memory_usage_factor = 0.07
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
return model_base.PiD(self, device=device)
|
||||
|
||||
class WAN21_T2V(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"image_model": "wan2.1",
|
||||
@ -2111,6 +2178,8 @@ models = [
|
||||
CosmosI2VPredict2,
|
||||
ZImagePixelSpace,
|
||||
ZImage,
|
||||
PiD,
|
||||
PixelDiTT2I,
|
||||
Lumina2,
|
||||
WAN22_T2V,
|
||||
WAN21_CausalAR_T2V,
|
||||
|
||||
104
comfy/text_encoders/pixeldit.py
Normal file
104
comfy/text_encoders/pixeldit.py
Normal file
@ -0,0 +1,104 @@
|
||||
import torch
|
||||
|
||||
from comfy import sd1_clip
|
||||
from .lumina2 import Gemma2BTokenizer, LuminaModel
|
||||
import comfy.text_encoders.llama
|
||||
|
||||
|
||||
class PixelDiTGemma2_2BModel(sd1_clip.SDClipModel):
|
||||
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
|
||||
llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
|
||||
if llama_quantization_metadata is not None:
|
||||
model_options = model_options.copy()
|
||||
model_options["quantization_metadata"] = llama_quantization_metadata
|
||||
|
||||
super().__init__(
|
||||
device=device, layer=layer, layer_idx=layer_idx,
|
||||
textmodel_json_config={}, dtype=dtype,
|
||||
special_tokens={"start": 2, "pad": 0},
|
||||
layer_norm_hidden_state=False,
|
||||
model_class=comfy.text_encoders.llama.Gemma2_2B,
|
||||
enable_attention_masks=attention_mask,
|
||||
return_attention_masks=attention_mask,
|
||||
model_options=model_options,
|
||||
)
|
||||
|
||||
|
||||
_PIXELDIT_CHI_PROMPT = (
|
||||
'Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions '
|
||||
"suitable for image generation. Evaluate the level of detail in the user prompt:\n"
|
||||
"- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, "
|
||||
"and spatial relationships to create vivid and concrete scenes.\n"
|
||||
"- If the prompt is already detailed, refine and enhance the existing details slightly without "
|
||||
"overcomplicating.\n"
|
||||
"Here are examples of how to transform or refine prompts:\n"
|
||||
"- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, "
|
||||
"sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.\n"
|
||||
"- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring "
|
||||
"glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus "
|
||||
"passing by towering glass skyscrapers.\n"
|
||||
"Please generate only the enhanced description for the prompt below and avoid including any "
|
||||
"additional commentary or evaluations:\n"
|
||||
"User Prompt: "
|
||||
)
|
||||
|
||||
_PIXELDIT_MAX_LENGTH = 300
|
||||
_PIXELDIT_CHI_PROMPT_DETECT_PREFIX = 'Given a user prompt, generate an "Enhanced prompt"'
|
||||
|
||||
|
||||
class PixelDiTGemma2Tokenizer(sd1_clip.SD1Tokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data=None):
|
||||
if tokenizer_data is None:
|
||||
tokenizer_data = {}
|
||||
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data,
|
||||
name="gemma2_2b", tokenizer=Gemma2BTokenizer)
|
||||
|
||||
def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
|
||||
if not text.strip():
|
||||
return super().tokenize_with_weights("", return_word_ids=return_word_ids, disable_weights=True, min_length=_PIXELDIT_MAX_LENGTH)
|
||||
|
||||
chi_token_count = len(self.gemma2_2b.tokenizer(_PIXELDIT_CHI_PROMPT)["input_ids"])
|
||||
combined = text if text.startswith(_PIXELDIT_CHI_PROMPT_DETECT_PREFIX) else _PIXELDIT_CHI_PROMPT + text
|
||||
max_length_all = chi_token_count + _PIXELDIT_MAX_LENGTH - 2
|
||||
out = super().tokenize_with_weights(combined, return_word_ids=return_word_ids,
|
||||
disable_weights=True, min_length=max_length_all)
|
||||
out["gemma2_2b"] = [out["gemma2_2b"][0][:max_length_all]]
|
||||
return out
|
||||
|
||||
def untokenize(self, token_weight_pair):
|
||||
return self.gemma2_2b.untokenize(token_weight_pair)
|
||||
|
||||
def state_dict(self):
|
||||
return self.gemma2_2b.state_dict()
|
||||
|
||||
|
||||
class PixelDiTGemma2TE(LuminaModel):
|
||||
# PixelDiT's select_index: keep BOS + last 299 embeddings of the padded sequence.
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
super().__init__(device=device, dtype=dtype, name="gemma2_2b",
|
||||
clip_model=PixelDiTGemma2_2BModel, model_options=model_options)
|
||||
|
||||
def encode_token_weights(self, token_weight_pairs):
|
||||
result = super().encode_token_weights(token_weight_pairs)
|
||||
cond, pooled = result[0], result[1]
|
||||
extra = result[2] if len(result) > 2 else None
|
||||
if cond.shape[1] > _PIXELDIT_MAX_LENGTH:
|
||||
cond = torch.cat([cond[:, :1], cond[:, -(_PIXELDIT_MAX_LENGTH - 1):]], dim=1)
|
||||
if extra is not None and "attention_mask" in extra:
|
||||
am = extra["attention_mask"]
|
||||
extra["attention_mask"] = torch.cat([am[..., :1], am[..., -(_PIXELDIT_MAX_LENGTH - 1):]], dim=-1)
|
||||
if extra is not None:
|
||||
return cond, pooled, extra
|
||||
return cond, pooled
|
||||
|
||||
|
||||
def pixeldit_te(dtype_llama=None, llama_quantization_metadata=None):
|
||||
class PixelDiTTE_(PixelDiTGemma2TE):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
if llama_quantization_metadata is not None:
|
||||
model_options = model_options.copy()
|
||||
model_options["llama_quantization_metadata"] = llama_quantization_metadata
|
||||
if dtype_llama is not None:
|
||||
dtype = dtype_llama
|
||||
super().__init__(device=device, dtype=dtype, model_options=model_options)
|
||||
return PixelDiTTE_
|
||||
55
comfy_extras/nodes_pid.py
Normal file
55
comfy_extras/nodes_pid.py
Normal file
@ -0,0 +1,55 @@
|
||||
"""PiD (Pixel Diffusion Decoder) node"""
|
||||
|
||||
import torch
|
||||
from typing_extensions import override
|
||||
|
||||
import node_helpers
|
||||
import comfy.latent_formats
|
||||
from comfy_api.latest import ComfyExtension, io
|
||||
|
||||
|
||||
class PiDConditioning(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="PiDConditioning",
|
||||
display_name="PiD Conditioning",
|
||||
category="advanced/conditioning",
|
||||
description=(
|
||||
"Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling"
|
||||
),
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Latent.Input("latent", tooltip="latent (from VAEEncode or a KSampler)."),
|
||||
io.Combo.Input("latent_format", options=["flux", "sd3"], default="flux",
|
||||
tooltip="Flux1 and Flux2 latents auto-detected from channel dim, sd3 has to be selected manually."),
|
||||
io.Float.Input(
|
||||
"degrade_sigma", default=0.0, min=0.0, max=1.0, step=0.01,
|
||||
tooltip="0 = clean latent. Increase to denoise corrupted latent outputs.",
|
||||
),
|
||||
],
|
||||
outputs=[io.Conditioning.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, positive, latent, latent_format: str, degrade_sigma: float) -> io.NodeOutput:
|
||||
samples = latent["samples"]
|
||||
if latent_format == "flux":
|
||||
fmt_cls = comfy.latent_formats.Flux2 if samples.shape[1] == 128 else comfy.latent_formats.Flux
|
||||
else:
|
||||
fmt_cls = comfy.latent_formats.SD3
|
||||
lq_latent = fmt_cls().process_in(samples)
|
||||
sigma_t = torch.tensor([float(degrade_sigma)], dtype=torch.float32)
|
||||
return io.NodeOutput(node_helpers.conditioning_set_values(
|
||||
positive, {"lq_latent": lq_latent, "degrade_sigma": sigma_t},
|
||||
))
|
||||
|
||||
|
||||
class PiDExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
||||
return [PiDConditioning]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> PiDExtension:
|
||||
return PiDExtension()
|
||||
5
nodes.py
5
nodes.py
@ -969,7 +969,7 @@ class CLIPLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
|
||||
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox", "lens"], ),
|
||||
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit"], ),
|
||||
},
|
||||
"optional": {
|
||||
"device": (["default", "cpu"], {"advanced": True}),
|
||||
@ -979,7 +979,7 @@ class CLIPLoader:
|
||||
|
||||
CATEGORY = "advanced/loaders"
|
||||
|
||||
DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b"
|
||||
DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\n pixeldit: gemma 2 2B elm"
|
||||
|
||||
def load_clip(self, clip_name, type="stable_diffusion", device="default"):
|
||||
clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
|
||||
@ -2420,6 +2420,7 @@ async def init_builtin_extra_nodes():
|
||||
"nodes_context_windows.py",
|
||||
"nodes_qwen.py",
|
||||
"nodes_chroma_radiance.py",
|
||||
"nodes_pid.py",
|
||||
"nodes_model_patch.py",
|
||||
"nodes_easycache.py",
|
||||
"nodes_audio_encoder.py",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user